RL Simulator Examples 🧠

Here are some classic reinforcement learning (RL) scenarios implemented in our RL Simulator:

1. Q-Learning Grid World

A basic maze navigation task where an agent learns optimal paths using Q-tables

🔗 [Explore Q-Learning Theory](/en/tech/ai/tutorials/rl_simulator/overview)

2. Deep Q-Network (DQN)

A neural network-based approach with experience replay and target networks

🧮 [Check Math Behind DQN](/en/tech/ai/tutorials/rl_simulator/math)

3. Policy Gradients

A model-free method that directly optimizes policy functions

📊 [Compare RL Algorithms](/en/tech/ai/tutorials/rl_simulator/comparison)

4. Multi-Agent Coordination

Simultaneous learning in environments with multiple interacting agents

🌍 [View Global RL Use Cases](/en/tech/ai/tutorials/rl_simulator/case_studies)